- Data, features, and embeddings - Data awareness - Modeling prior knowledge - The curse of dimensionality - Task-driven features and invariances - Recap of linear algebra - Vector spaces, bases - Linear maps - Matrix notation and matrix algebra - Tensors and tensor operations - Parametric models and regression - Linear and polynomial regression - Convexity and Lp norms - Underfitting and overfitting - Cross validation - Logistic regression - Optimization - Gradient descent - Stochastic gradient descent - Learning rate, decay, momentum, batch size - Forward and reverse-mode automatic differentiation - Deep neural networks - Multi layer perceptron - Backpropagation - Universal approximation theorems - Autograd and modules - Invariance, equivariance, compositionality - Convolutional neural networks - Pooling - Double descent - Regularization: weight penalty, early stopping, dropout, batchnorm - Generative models - PCA - Manifolds and the manifold hypothesis - Representation learning - Autoencoders: variational, contractive, denoising - Generative adversarial networks - Adversarial learning - Decision boundaries - Black-box and white-box attacks - Adversarial perturbations: universal and one-pixel - Adversarial training - Geometric deep learning - Learning on graphs and point clouds - Learning on surfaces - Generative models of structured data - Adversarial surfaces

Data la natura altamente dinamica dell'area coperta da questo corso avanzato, non è previsto un testo unico di riferimento. Durante il corso verranno indicate e fornite di volta in volta le fonti sotto forma di articoli scientifici e capitoli di libri. Come riferimento generale, i seguenti libri possono rivelarsi utili: Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville MIT Press, 2016 Deep Learning with PyTorch Vishnu Subramanian Packt, 2018